class_docstr: A preprocessing layer which randomly adjusts contrast during training.
  
  This layer will randomly adjust the contrast of an image or images
  by a random factor. Contrast is adjusted independently
  for each channel of each image during training.
  
  For each channel, this layer computes the mean of the image pixels in the
  channel and then adjusts each component `x` of each pixel to
  `(x - mean) * contrast_factor + mean`.
  
  Input pixel values can be of any range (e.g. `[0., 1.)` or `[0, 255]`) and
  in integer or floating point dtype.
  By default, the layer will output floats.
  
  **Note:** This layer is safe to use inside a `tf.data` pipeline
  (independently of which backend you're using).
  
  Input shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.
  
  Output shape:
      3D (unbatched) or 4D (batched) tensor with shape:
      `(..., height, width, channels)`, in `"channels_last"` format.
  
  Args:
      factor: a positive float represented as fraction of value, or a tuple of
          size 2 representing lower and upper bound.
          When represented as a single float, lower = upper.
          The contrast factor will be randomly picked between
          `[1.0 - lower, 1.0 + upper]`. For any pixel x in the channel,
          the output will be `(x - mean) * factor + mean`
          where `mean` is the mean value of the channel.
      seed: Integer. Used to create a random seed.
code: def _adjust_constrast(self, inputs, contrast_factor):
      # reduce mean on height
      inp_mean = self.backend.numpy.mean(inputs, axis=-3, keepdims=True)
      # reduce mean on width
      inp_mean = self.backend.numpy.mean(
          inp_mean, axis=-2, keepdims=True
      )
  
      outputs = (inputs - inp_mean) * contrast_factor + inp_mean
      return outputs
  
file_path: keras/keras/layers/preprocessing/random_contrast.py
imports: from keras.api_export import keras_export
  from keras.layers.preprocessing.tf_data_layer import TFDataLayer
  from keras.random.seed_generator import SeedGenerator
label_desc: Use image node_id 3 for calling the RandomContrast obj's underlying member method code with example usage: obj._adjust_constrast(inputs, contrast_factor) and returns: outputs